Resources for Teaching and Learning with AI

AI in Higher Education

The following resources focus on an overview of AI in higher education. We include two glossaries of common AI terms. 

• US Department of Education: Artificial Intelligence and the Future of Teaching and Learning - Summarizes the opportunities and risks for AI in teaching, learning, research, and assessment based on public input. 
• EDUCAUSE Special Report | Artificial Intelligence: Where Are We Now? - Provides an overview of AI including the use of AI applications in higher education, its promises and perils, ethical implications, and its role in ensuring student success.
• MLA-CCCC Joint Task Force on Writing and AI Working Paper: Overview of the Issues, Statement of Principles, and Recommendations - Discusses the risks and benefits of generative AI for teachers and students in writing, literature, and language programs. Makes recommendations for developing policies and critical AI literacy.
• TeachOnline.ca: AI and the Future of Teaching and Learning- An overview of the uses of AI in higher education, as well as common pitfalls and recommendations for future development.

Glossaries of AI terms

• Coursera: Artificial Intelligence (AI) Terms: A to Z Glossary - Common Artificial Intelligence terms.
• CIRCLS: Glossary of Artificial Intelligence Terms for Educators - Created by the Center for Integrative Research in Computing and Learning Sciences (CIRCLS) for educators to reference when learning about and using AI.

AI Sample Syllabus Statements 

The following AI syllabus statements and classroom policies were created by educators at various higher education institutions. Statements include options for no use of AI, limited use, and extensive use, depending on the course and instructor.

• Colorado State: What should a syllabus statement on AI look like?
• Texas A&M University: Generative AI Syllabus Statement Considerations
• University of Vermont - Writing in the Disciplines: Chat GPT Syllabus Statements
• University of Minnesota: ChatGPT Syllabus Statements
• Syllabus Statement Builder - Pepperdine's Generative AI Statement (Policy) builder for your syllabus. No AI is involved, and this will assist you in creating the perfect syllabus statement for your course.

AI and Academic Integrity

The following resources address concerns regarding academic integrity when using AI. We include AI citation guidelines.

• Cornell University: AI and Academic Integrity - Provides guidance for instructors to address AI and academic integrity in the classroom.
• Faculty Focus: Essential Considerations for Addressing the Possibility of AI-Driven Cheating, Part 1 - The author, Torrey Trust, addresses educators' concerns regarding students' potential use of AI for cheating.
• Faculty Focus: Essential Considerations for Addressing the Possibility of AI-Driven Cheating, Part 2 - Part 2 discusses how instructors can redesign assignments using the TRUST model to reduce opportunities for cheating.
• Considerations of AI Detection [PDF] - This accessible PDF is based on the original, Considerations of AI Detection - CSU Channel Islands (Google Slides) - From CSU Channel Islands Teaching and Learning Innovations, provides an overview of AI detection tools and their limitations.

AI Citation Guidelines

The following resources provide guidelines for citing and referencing ChatGPT and other AI tools using the style guide appropriate to your discipline:

• American Psychological Association: How to cite ChatGPT 
• Modern Language Association: How do I cite generative AI in MLA style?
• The Chicago Manual of Style Online: Citation, Documentation of Sources

AI and Bias

The following resources provide an overview of bias in AI systems. AI bias refers to the tendency of algorithms to reflect human biases, since output is based on human-generated AI training data and reflects historical and social inequities. 

• National Institute of Standards and Technology (NIST): There’s More to AI Bias Than Biased Data -  NIST Report (2022) - Highlights how bias appears not just in AI algorithms and their training data, but also in the way AI systems are used in society.
• The Radical AI Podcast: More than a Glitch, Technochauvanism, and Algorithmic Accountability with Meredith Broussard - An interview with Meredith Broussard, author of More than a Glitch.
• NYU Center for Disability Studies: Disability Bias and AI - Links to a 2019 report: Disability Bias and AI that identifies key questions regarding disability and the social implications of AI, including concerns about bias and reinforcing marginalization. 
• Penn State: AI language models show bias against people with disabilities - Describes findings from a Penn State study showing that all the algorithms and models they tested contained significant implicit bias against people with disabilities.

Books foundational to AI Bias research

• Race After Technology - Ruha Benjamin - Benjamin examines various technologies, ranging from common apps to complicated algorithms, and explores how these new technologies can reinforce White supremacy and make social inequality worse.
• Algorithms of Oppression - Safiya Umoja Noble - Noble describes data discrimination as a real social problem. She argues that search algorithms are biased, privilege whiteness, and discriminate against people of color, specifically women of color.
• More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech - Meredith Broussard - Broussard argues that racism, sexism, and ableism aren't just “glitches” in well-functioning AI systems; she argues they are coded into the systems themselves.

AI Ethical Concerns and Challenges

The following resources provide an overview of ethical concerns and challenges regarding AI systems and their use, including privacy and security, copyright and data ownership, inaccuracies in output, and more.

• Santa Clara University: Artificial Intelligence and Ethics: Sixteen Challenges and Opportunities - Provides a description of 16 ethical concerns regarding AI use and development, with links to further resources.
• eWeek: Generative AI Ethics: Concerns and Solutions - Provides an overview of the most prevalent concerns regarding the use of generative AI such as privacy and security, copyright and data ownership, transparencies in training processes, inaccuracies in output, and more. Includes emerging policies and legislation.
• IBM: What is AI Ethics? - Provides a comprehensive view of AI ethics, including principles, primary concerns, possible solutions, and links to further resources.

Teaching with AI

The following resources provide examples of teaching with AI in the higher education classroom.

• OSU: AI: Considerations for Teaching and Learning - Created by Ohio State University, provides an overview of Generative AI benefits and limitations, as well as teaching strategies and examples.
• Understanding AI Writing Tools and Their Uses for Teaching and Learning at UC Berkeley - Includes an overview of ChatGPT, teaching recommendations, and suggested writing prompts and activities.
• Artificial Intelligence Tools: Bloom's Taxonomy Revisited - Created by Oregon State University, provides a revised version of Bloom's Taxonomy incorporating the use of AI, and provides examples. 
•  A Teacher's Prompt Guide to ChatGPT [PDF] - This is an accessible version of the original: A Teacher's Prompt Guide to ChatGPT (original). Developed by Andrew Herft, Curriculum Advisor for New South Wales Department of Education. Provides guidance on using ChatGPT efficiently and effectively to enhance teaching and learning. Some examples apply to K-12 and can be adapted for higher ed.

AI and Digital Literacy

The following resources provide guidance on building AI literacy skills, including understanding how technologies like machine learning and generative AI work, how they can be used for problem-solving, and the technology’s consequences.

• What I Mean When I Say Critical AI Literacy from Maha Bali's Blog on Education - Bali is a Professor of Practice at the American University in Cairo (Maha's Bio) - Provides a definition of AI Literacy, includes suggestions for using AI in the classroom to promote literacy, and links to further resources.
• Critical Media Literacy Guides - Derived from Critical Media Literacy and Civic Learning. Includes a section, Teacher and Student Guide to Analyzing AI Writing Tools, that provides a list of analysis questions regarding the AI tools themselves, as well as questions about the text produced by the tools.
• TextGenEd: An Introduction to Teaching with Text Generation Technologies - Created by the Writing Across the Curriculum (WAC) Clearinghouse - Provides a collection of assignments focused on AI Literacy development. The collection is accessible to teachers with different levels of comfort using technologies.
• Supporting AI Literacy for Educators: New and Emerging Resources - Created by Digital Promise, provides resources to support educators in developing AI literacies.  
• Cornell Center for Teaching Innovation: Ethical AI for Teaching and Learning - Guidance on building literacy in Generative AI including understanding, evaluating, and becoming familiar with the uses of generative AI tools.

CSU Campus AI Resources

The following resources were developed by educators at other CSU campuses. We will continue to update this section as more campuses create and share their resources.

• CSU Academic Senate AI Working Group: Renewing the Call for a Working Group on Artificial Intelligence (AI) in Higher Education
• San Jose State: Generative AI and ChatGPT: Resources for Instructors
• CSU Monterey Bay: AI Resources for Faculty
• San Francisco State: Information and Resources Regarding ChatGPT
• CSU Long Beach: Chatbots and Beyond: A Guide about AI Technologies
• CSU Fullerton: Artificial Intelligence